Answering Legal Questions with LLMs

Scope and Complexity of Law

• Many commenters doubt LLMs can correctly answer legal questions because real-world law is highly interconnected: statutes cross-reference, depend on regulations, guidance, case law, and local practice.
• GDPR and EU law are cited as examples where you must read statutes together with opinions, guidelines, and court decisions.
• Several argue that for genuinely hard legal questions, identifying which materials even matter is itself expert work; if a lawyer must curate all inputs, they might as well write the answer.

RAG, Context Windows, and Technical Limits

• There is extensive debate on Retrieval-Augmented Generation:
  • Supporters say RAG is ideal for legal/medical use: constrain answers to uploaded documents, provide citations, and dramatically reduce hallucinations.
  • Skeptics counter that RAG only helps; LLMs still hallucinate, especially at long context lengths or when instructions get diluted.
  • Some call RAG “as safe as SQL,” others strongly dispute this, noting legal facts and reasoning don’t map neatly to product-catalog–style databases.
• Long-context models (e.g., Gemini 1.5) are praised for handling huge manuals, but people note quality still degrades with very large prompts.

Role of LLMs in Legal Practice

• Broad consensus: LLMs should be tools “in service of” lawyers, not standalone advisors to end users.
• Current high‑value use cases:
  • Summarizing and simplifying legal language (with human review).
  • Acting as a “junior associate” / research assistant / personal librarian to surface relevant passages and cases.
• Direct, unreviewed use for filings has already produced public failures (made‑up case law), reinforcing the need for human verification and professional liability concerns.

Trust, UX, and Product Design

• Many want systems that foreground sources and minimize “answer-y” prose, to discourage blind trust and reduce automation bias.
• Some suggest semantic/embedding search plus highlighted passages may be safer than full generative answers.
• Others highlight that human laziness is the unsolved problem: even with citations, many users won’t rigorously check.

Democratization vs. Inequality and Systemic Effects

• One camp expects LLMs to democratize access to legal information, enabling cheap complaints, Q&A, translation, and assistance for less powerful parties.
• Another worries laws will grow more complex once elites can offload complexity to AI, further advantaging those with the best tools and deepening opacity.
• There is discussion of potential arms races (dueling legal AIs) and protectionism by legal institutions.

Comparisons to Other Professions

• Programming is widely seen as an area where LLMs already add clear value; that success fuels optimism for law and medicine.
• Others draw analogies to self-driving cars: getting from “pretty good” to the reliability needed in safety‑critical domains may be a very long tail.